The invention relates to a method for the transformation of image signals that have been obtained by color filtering and have been logarithmically compressed, wherein the color saturation of the recorded images is modified. The invention furthermore relates to a saturation stage for carrying out the method and also to a digital camera having such a saturation stage.
In photographic and film camera technology, electronic image recorders, which convert an optical intensity distribution into electronic image signals, are increasingly being used as a replacement for conventional film material. Such image recorders have a regular arrangement of pixels which are each assigned one or more light-sensitive circuits comprising semi-conductor components, these circuits hereinafter being referred to as image cells. Each of these image cells generates an image signal whose voltage value is a function of the intensity of the light impinging on the image cell.
In image recorders for color reproduction, each pixel generally comprises a triad of image cells which are each covered by a color filter for one of the three spectral colors red, green and blue. Each signal of such an image cell reproduces a brightness value relative to the relevant spectral color, so that the totality of the three individual signals contains the color information for the relevant pixel.
If an image represented by such image signals is viewed directly on a monitor, then the result generally deviates more or less significantly from the actual visual impression gained by a person by directly viewing the recorded motif. Therefore, the image signals are generally digitized and, in digital signal processors, subjected to different transformations in order to adapt the recorded images to the actual visual impression.
Such transformations can be used for example to remove color casts (color transformations) or to brighten or darken recorded images overall (brightness transformations). Furthermore, it is possible to modify the color saturation of such electronic images. The saturation of a color is understood here as the difference between the color value and a grey-scale value of the same brightness. Weakly saturated colors are therefore pale or even greyish, while strongly saturated colors have a powerful and brilliant effect.
The description of such transformations is usually based on the so-called RGB color model, since this largely corresponds to the method of operation of image recorders and color monitors. This is because both in the RGB color model and in image recorders and color monitors colors are reproduced by components of the three spectral colors red, green and blue, which can each assume values between 0 and 1 in the color model. In this way, the totality of the representable colors can be represented in a unit cube spanned by a coordinate system on whose axes the three color components are plotted. If the components of the three primary colors have the same magnitude, which corresponds to a point on a spatial diagonal of the unit cube, then a pure grey-scale value is obtained. In the case of a weakly saturated color, the point representing this color lies in the vicinity of this spatial diagonal, i.e. the components of the spectral colors deviate only slightly from one another.
A transformation for the saturation of RGB colors is known from a paper by Paul Haeberli from 1993, which was published on the Internet under the address http://wwp.sqi.com/graphica/matrix/index.html. If R designates an image signal for the spectral color red at a specific pixel, then the transformed image signal R′ is produced, after the transformation described there, from the equationR′=α·(R−L)+L,where L designates a brightness value for the relevant pixel and α designates a saturation factor. Corresponding equations apply with regard to the transformed image signals G′ and B′ for the spectral color green and blue, respectively, the saturation factor α and the brightness value L being identical for all the spectral colors of a pixel. In this case, the brightness value L is determined according to the equationL=R·WR+G·WG+B·WBwhere                WR=0.3086,        WG=0.6094 and        WB=0.0820.        
If the saturation factor α is chosen to be less than 1, then this leads to a reduction of the color saturation. Saturation factors α which are greater than 1 produce more strongly saturated colors.
The paper furthermore points out that this transformation leads to correct results only when the image signals R, G and B are linear. Linear image signals are distinguished by the fact that there is a linear relationship between the voltage value of such an image signal and the optical intensity which impinges on the relevant pixel. This is the case for example with the image recorders using CCD technology (CCD=charge coupled device) that are often used in today video cameras. By contrast, if linear image signals are not involved, then according to Haeberli these signals must first be converted into linear signals before it is possible to carry out the above-described transformation for altering the color saturation.
EP 0 632 930 B1 discloses an image recorder which compresses a high input signal dynamic range logarithmically to a considerably smaller output signal dynamic range. Each pixel of this known image recorder thus generates an output voltage which corresponds to the logarithm of the optical intensity impinging thereon. As a result, the extremely high irradiance dynamic range of natural scenes, which is of the order of magnitude of 120 dB, can be acquired by signal technological means. Such an image recorder can thus be used to electronically acquire images whose brightness dynamic range comes extremely close to the actual visual perception of humans. This is primarily due to the fact that the human eye also has an approximately logarithmic visual sensitivity.
While these logarithmically compressed image signals reproduce a brightness dynamic range of about 120 dB, the absolute differences between the image signals of the individual spectral colors are comparatively small, however. It has the result that the images recorded using the known image recorder often have an inadequate color saturation. It therefore appears to be possible to follow the suggestion made in the paper by P. Haeberli cited above and firstly to linearize again the logarithmically compressed image signals after digitization, then to transform them in the manner described there and subsequently to logarithmize them again. However, such linearization (i.e. delogarithmization) and subsequent logarithmization of the image signals is highly complex computationally and can therefore be achieved only with expensive digital signal processors.